CE 345 | Course Introduction and Application Information - Department of Computer Engineering

Course Name

Introduction to Machine Learning

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
CE 345	Fall/Spring	3	0	3	5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	First Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	Discussion Problem Solving Q&A Lecture / Presentation
National Occupation Classification	-
Course Coordinator	Dr. Öğr. Üyesi Alper Demir
Course Lecturer(s)	Dr. Öğr. Üyesi M. Çağkan ULUDAĞLI
Assistant(s)	-

Course Objectives	The field of machine learning is concerned with the question of how to construct computer programs that improve automatically with experience. In recent years, many successful applications of machine learning have been developed, ranging from data-mining programs that learn to detect fraudulent credit card transactions, to autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundation of this field. The goal of this course is to provide an overview of the state-of-art algorithms used in machine learning. Both the theoretical properties of these algorithms and their practical applications will be discussed.
Learning Outcomes	The students who succeeded in this course; will be able to distinguish between a range of machine learning techniques. will be able to apply the basic techniques/algorithms of the field. will be able to compare various techniques/algorithms of the field. will be able to design various Machine Learning algorithms to specific situations. will be able to evaluate potential applications of Machine Learning techniques.
Course Description	Machine learning is concerned with computer programs that automatically improve their performance with past experiences. Machine learning draws inspiration from many fields, artificial intelligence, statistics, information theory, biology and control theory. The course will cover the following topics; computational learning theory, machine learning concepts, Bayesian learning, supervised learning, classification methods, regression methods, unsupervised learning, clustering methods, artificial neural networks, reinforcement learning, and discussion of advanced machine learning methods.
Related Sustainable Development Goals

Course Category	Core Courses
	Major Area Courses	X
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

Week	Subjects	Related Preparation
1	Introduction to Data Science with Python	Grus, Ch.s 2--6
2	Introduction and Machine Learning Concepts	Alpaydın, Ch.1
3	Bayesian Decision Theory and Classification	Alpaydın, Ch.3
4	Supervised Learning - Parametric Classification Methods	Alpaydın, Ch.s 2, 10; Goodfellow et al, Ch. 5.5
5	Supervised Learning - Non-parametric Classification Methods	Hastie et al, Ch. 13
6	Supervised Learning - Regression Methods	Weisberg, Ch. 2
7	Machine Learning Metrics	Various articles and studies
8	Midterm Exam
9	Unsupervised Learning - Clustering Methods	Alpaydın, Ch. 7; Geron, Ch. 9
10	Unsupervised Learning - Clustering Methods	Geron, Ch. 9; Murphy, Ch.s 25.3, 25.4, 25.5
11	Unsupervised Learning - Neural Networks	Bishop, Ch. 5; Alpaydın, Ch. 11; Hastie et al, Ch. 11
12	Unsupervised Learning - Neural Networks	Bishop, Ch. 5; Alpaydın, Ch. 11; Hastie et al, Ch. 11
13	Reinforcement Learning	Alpaydın, Ch. 18
14	Reinforcement Learning and Advanced Machine Learning Methods	Alpaydın, Ch.s 11, 18; Goodfellow et al, Ch.s 6, 7, Murphy, Ch. 28
15	Semester review
16	Final Exam

Course Notes/Textbooks

Alpaydın, E. (2014), Introduction to Machine Learning. The MIT Press, ISBN-13: 978-0-262-028189

Suggested Readings/Materials

Grus, J. (2019). Data science from scratch: first principles with python. O'Reilly Media, ISBN: 9781492041139

Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press, ISBN-13: 978-0262018029

Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, ISBN: 0070428077

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, ISBN-13: 978-0387-31073-2

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer, ISBN-13: 978-0-387-84857-0

Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc., ISBN-13: 9781492032649

Weisberg, S. (2014). Applied linear regression. Wiley, ISBN-13: 9780471663799

Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning. MIT Press, ISBN-13: 978-0262035613

Semester Activities	Number	Weigthing
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques	6	30
Portfolio
Homework / Assignments
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	7	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	14	4	56
Field Work			0
Quizzes / Studio Critiques	6	2	12
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	14	14
Final Exam	1	20	20
		Total	150

#	Program Competencies/Outcomes	* Contribution Level
#	Program Competencies/Outcomes	1	2	3	4	5
1	To have adequate knowledge in Mathematics, Science and Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems.	-	-	-	-	-
2	To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose.	-	-	X	-	-
3	To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose.	-	-	-	-	-
4	To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively.	-	-	-	-	X
5	To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics.	-	-	X	-	-
6	To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary teams; to be able to work individually.	-	-	-	-	-
7	To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively, to be able to give and receive clear and comprehensible instructions.	-	-	-	-	-
8	To have knowledge about global and social impact of Computer Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Computer Engineering solutions.	-	-	-	-	-
9	To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications.	-	-	-	-	-
10	To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development.	-	-	-	-	-
11	To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1)	-	-	-	-	-
12	To be able to speak a second foreign language at a medium level of fluency efficiently.	-	-	-	-	-
13	To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Computer Engineering.	-	-	-	-	-

CE 345 | Course Introduction and Application Information

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